Muhammad, Naveed, juhendajaFishman, Dmytro, juhendajaZabolotnii, DmytroTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Arvutiteaduse instituut2023-09-142023-09-142021https://hdl.handle.net/10062/92197Autonomous Vehicles (AV) research moves forward and promises to create a safer and more efficient driving process than the vast majority of humans can achieve. However, just as humans, Autonomous Vehicles still rely on the maps of their surroundings to conduct most of their operational sub-tasks. These maps are enriched with a large quantity of additional information for a more accurate representation of the natural world, earning the common name of High Definition (HD) Map. The rapid increase of the field’s popularity also brings a great deal of attention to the HD maps creation and maintenance. Still, to this day, almost all HD maps are created using many human hours of expert labor, raising their cost and creating barriers to broader adoption. In this work, we research recent advancements of HD maps automatic creation and apply novel methods to extract road information, namely road boundaries. We strive to create an automatic system capable of extracting the necessary information from LIDAR data from vehicles deployed in urban conditions, with a high degree of accuracy and tolerance to externalities, such as weather conditions or road construction details. In order to evaluate the system, we use the publicly available Nuscenes dataset and compare automatically created road boundaries with provided manually drafted ground truth. The system achieves a precision score of 0.62 and a recall score of 0.31 at the distance tolerance of 40 cm.engopenAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalautonomous drivinghigh definition mapscomputer visionpoint cloud’s processingmagistritöödinformaatikainfotehnoloogiainformaticsinfotechnologyThesis